Time-Series Prediction of the Oscillatory Phase of EEG Signals Using the Least Mean Square Algorithm-Based AR Model

Aqsa Shakeel,Toshihisa Tanaka,Keiichi Kitajo

doi:10.3390/app10103616

Abstract

Neural oscillations are vital for the functioning of a central nervous system because they assist in brain communication across a huge network of neurons. Alpha frequency oscillations are believed to depict idling or inhibition of task-irrelevant cortical activities. However, recent studies on alpha oscillations (particularly alpha phase) hypothesize that they have an active and direct role in the mechanisms of attention and working memory. To understand the role of alpha oscillations in several cognitive processes, accurate estimations of phase, amplitude, and frequency are required. Herein, we propose an approach for time-series forward prediction by comparing an autoregressive (AR) model and an adaptive method (least mean square (LMS)-based AR model). This study tested both methods for two prediction lengths of data. Our results indicate that for shorter data segments (prediction of 128 ms), the AR model outperforms the LMS-based AR model, while for longer prediction lengths (256 ms), the LMS- based AR model surpasses the AR model. LMS with low computational cost can aid in electroencephalography (EEG) phase prediction (alpha oscillations) in basic research to reveal the functional role of the oscillatory phase as well as for applications for brain-computer interfaces.

Highlights

Changing neural oscillations are fundamental features of a working central nervous system.These oscillations can be seen as rhythmic changes either in cellular spiking behavior or subthreshold membrane potential in a single neuron
This study focuses on the forward prediction error and demonstrates that the least mean square (LMS)-based AR model minimizes the forward prediction error because its coefficients can be adjusted dynamically, and it performs the AR model better in real-time for long prediction lengths
We wanted to determine the performance of both methods to assess the phase-locking value (PLV) at different time points [42]

Summary

Introduction

Changing neural oscillations are fundamental features of a working central nervous system. These oscillations can be seen as rhythmic changes either in cellular spiking behavior or subthreshold membrane potential in a single neuron. Large ensembles of these neurons can generate synchronous activity that results in rhythmic oscillations in the local field potential (LFP). These oscillations reflect the excitability of neurons. Alpha frequencies are slower and tend to distribute frontally in older subjects [6], the largest alpha amplitude is observed

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